from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

print_time_report()

Time report

daal4py_KMeans_short: 0h 0m 1s

daal4py_Ridge: 0h 0m 1s

KMeans_short: 0h 0m 2s

daal4py_LogisticRegression: 0h 0m 4s

daal4py_KMeans_tall: 0h 0m 8s

Ridge: 0h 0m 11s

LogisticRegression: 0h 0m 19s

KMeans_tall: 0h 0m 21s

daal4py_KNeighborsClassifier_kd_tree: 0h 0m 28s

daal4py_KNeighborsClassifier: 0h 2m 25s

KNeighborsClassifier_kd_tree: 0h 2m 35s

xgboost: 0h 5m 1s

catboost_symmetric: 0h 5m 2s

catboost_lossguide: 0h 5m 4s

lightgbm: 0h 5m 7s

HistGradientBoostingClassifier: 0h 5m 8s

KNeighborsClassifier: 0h 33m 52s

total: 1h 5m 57s

print_env_info()

Benchmark environment

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.197	0.000	4.064	0.000	1	1	NaN	NaN	0.489	0.000	0.403	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	15.530	0.266	0.000	0.016	1	1	0.726	0.808	1.722	0.007	9.018	0.159	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.222	0.001	0.000	0.222	1	1	1.000	1.000	0.088	0.001	2.530	0.023	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.157	0.000	5.100	0.000	-1	100	NaN	NaN	0.476	0.000	0.329	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	37.770	0.000	0.000	0.038	-1	100	0.933	0.934	1.769	0.004	21.348	0.053	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.216	0.015	0.000	0.216	-1	100	1.000	1.000	0.087	0.000	2.481	0.169	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.158	0.000	5.058	0.000	1	5	NaN	NaN	0.475	0.000	0.333	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	22.323	1.344	0.000	0.022	1	5	0.832	0.717	1.720	0.005	12.981	0.783	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.210	0.001	0.000	0.210	1	5	1.000	1.000	0.087	0.000	2.414	0.009	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.132	0.000	6.080	0.000	1	100	NaN	NaN	0.477	0.000	0.276	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	20.841	0.047	0.000	0.021	1	100	0.933	0.717	1.720	0.008	12.119	0.061	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.210	0.002	0.000	0.210	1	100	1.000	1.000	0.087	0.001	2.409	0.038	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.130	0.000	6.166	0.000	-1	5	NaN	NaN	0.477	0.000	0.272	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	33.381	0.000	0.000	0.033	-1	5	0.832	0.934	1.772	0.010	18.838	0.102	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.188	0.015	0.000	0.188	-1	5	1.000	1.000	0.087	0.000	2.147	0.176	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.128	0.000	6.227	0.000	-1	1	NaN	NaN	0.477	0.000	0.270	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	25.942	0.395	0.000	0.026	-1	1	0.726	0.808	1.719	0.005	15.087	0.233	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.174	0.016	0.000	0.174	-1	1	1.000	1.000	0.090	0.006	1.944	0.226	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.294	0.000	1	1	NaN	NaN	0.099	0.000	0.549	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	10.492	0.009	0.000	0.010	1	1	0.975	0.972	0.255	0.002	41.180	0.281	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.013	0.001	0.000	0.013	1	1	1.000	1.000	0.005	0.001	2.575	0.319	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.295	0.000	-1	100	NaN	NaN	0.099	0.000	0.546	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	31.675	0.000	0.000	0.032	-1	100	0.984	0.975	0.302	0.001	104.890	0.510	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.027	0.002	0.000	0.027	-1	100	1.000	1.000	0.005	0.001	5.290	0.705	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.297	0.000	1	5	NaN	NaN	0.099	0.000	0.543	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	19.087	0.044	0.000	0.019	1	5	0.980	0.963	0.254	0.001	75.286	0.259	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.021	0.001	0.000	0.021	1	5	1.000	1.000	0.005	0.000	4.214	0.379	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.295	0.000	1	100	NaN	NaN	0.099	0.000	0.551	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	19.145	0.015	0.000	0.019	1	100	0.984	0.963	0.254	0.002	75.265	0.593	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.022	0.001	0.000	0.022	1	100	1.000	1.000	0.005	0.000	4.365	0.398	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.297	0.000	-1	5	NaN	NaN	0.099	0.000	0.546	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	31.823	0.000	0.000	0.032	-1	5	0.980	0.975	0.301	0.001	105.750	0.197	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.028	0.003	0.000	0.028	-1	5	1.000	1.000	0.005	0.000	5.488	0.762	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.298	0.000	-1	1	NaN	NaN	0.099	0.000	0.544	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	23.216	0.062	0.000	0.023	-1	1	0.975	0.972	0.255	0.001	91.080	0.512	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.018	0.002	0.000	0.018	-1	1	1.000	1.000	0.005	0.000	3.628	0.506	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.721	0.000	0.029	0.000	1	1	NaN	NaN	0.692	0.000	3.935	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.747	0.005	0.000	0.001	1	1	0.966	0.956	0.108	0.001	6.912	0.084	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.446	3.178	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.720	0.000	0.029	0.000	-1	5	NaN	NaN	0.721	0.000	3.773	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.824	0.003	0.000	0.001	-1	5	0.981	0.976	0.200	0.001	4.128	0.029	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	9.667	5.562	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.676	0.000	0.030	0.000	-1	100	NaN	NaN	0.690	0.000	3.878	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.731	0.007	0.000	0.003	-1	100	0.984	0.977	0.592	0.005	4.611	0.042	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.005	0.001	0.000	0.005	-1	100	1.000	1.000	0.001	0.000	7.237	3.695	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.651	0.000	0.030	0.000	-1	1	NaN	NaN	0.691	0.000	3.836	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.433	0.003	0.000	0.000	-1	1	0.966	0.976	0.200	0.002	2.168	0.024	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	8.870	4.666	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.691	0.000	0.030	0.000	1	100	NaN	NaN	0.684	0.000	3.937	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.810	0.018	0.000	0.005	1	100	0.984	0.956	0.108	0.001	44.551	0.446	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	1	100	1.000	1.000	0.000	0.000	13.840	8.196	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.684	0.000	0.030	0.000	1	5	NaN	NaN	0.690	0.000	3.889	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.426	0.004	0.000	0.001	1	5	0.981	0.977	0.590	0.003	2.418	0.014	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.001	0.000	2.053	1.134	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.714	0.000	0.022	0.000	1	1	NaN	NaN	0.418	0.000	1.707	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.022	0.001	0.001	0.000	1	1	0.978	0.978	0.001	0.000	30.772	9.378	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.572	4.020	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.712	0.000	0.022	0.000	-1	5	NaN	NaN	0.453	0.000	1.573	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.025	0.001	0.001	0.000	-1	5	0.988	0.987	0.001	0.000	24.325	7.137	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	21.047	14.690	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.713	0.000	0.022	0.000	-1	100	NaN	NaN	0.417	0.000	1.709	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.040	0.002	0.000	0.000	-1	100	0.989	0.984	0.006	0.001	6.676	0.843	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	100	1.000	1.000	0.000	0.000	17.005	13.112	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.708	0.000	0.023	0.000	-1	1	NaN	NaN	0.419	0.000	1.690	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.023	0.001	0.001	0.000	-1	1	0.978	0.987	0.001	0.000	22.049	5.330	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	19.243	12.995	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.702	0.000	0.023	0.000	1	100	NaN	NaN	0.419	0.000	1.677	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.043	0.004	0.000	0.000	1	100	0.989	0.978	0.001	0.000	59.250	20.012	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	6.304	4.586	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.705	0.000	0.023	0.000	1	5	NaN	NaN	0.419	0.000	1.682	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.023	0.001	0.001	0.000	1	5	0.988	0.984	0.006	0.001	3.886	0.437	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.291	4.168	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.606	0.0	0.791	0.000	k-means++	NaN	30	NaN	0.422	0.0	1.438	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.378	0.000	k-means++	0.001	30	0.001	0.000	0.0	8.296	5.287	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	10.806	7.537	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.488	0.0	0.984	0.000	random	NaN	30	NaN	0.348	0.0	1.402	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.377	0.000	random	0.001	30	0.001	0.000	0.0	8.583	5.685	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	random	1.000	30	1.000	0.000	0.0	9.639	5.614	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.323	0.0	3.796	0.000	k-means++	NaN	30	NaN	2.959	0.0	2.137	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	15.774	0.000	k-means++	0.002	30	0.002	0.000	0.0	5.843	2.884	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.020	0.001	k-means++	1.000	30	1.000	0.000	0.0	10.431	7.052	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	5.813	0.0	4.129	0.000	random	NaN	30	NaN	2.791	0.0	2.083	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	15.871	0.000	random	0.001	30	0.002	0.000	0.0	5.776	2.967	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.020	0.001	random	1.000	30	1.000	0.000	0.0	9.931	6.917	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.212	0.0	0.015	0.000	k-means++	NaN	20	NaN	0.084	0.0	2.517	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.193	0.000	k-means++	0.001	20	0.000	0.001	0.0	2.714	0.411	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	9.366	5.458	See	See
3	KMeans_short	fit	10000	10000	2	20	0.072	0.0	0.044	0.000	random	NaN	20	NaN	0.029	0.0	2.488	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.194	0.000	random	-0.001	20	0.002	0.001	0.0	2.704	0.535	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	random	1.000	20	1.000	0.000	0.0	9.777	5.392	See	See
6	KMeans_short	fit	10000	10000	100	20	0.549	0.0	0.291	0.000	k-means++	NaN	20	NaN	0.305	0.0	1.803	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.809	0.000	k-means++	0.326	20	0.279	0.001	0.0	2.223	0.357	See	See
8	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	k-means++	1.000	20	1.000	0.000	0.0	8.187	3.814	See	See
9	KMeans_short	fit	10000	10000	100	20	0.188	0.0	0.851	0.000	random	NaN	20	NaN	0.123	0.0	1.532	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.816	0.000	random	0.283	20	0.258	0.001	0.0	2.217	0.378	See	See
11	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	random	1.000	20	1.000	0.000	0.0	8.032	3.941	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	10.893	0.0	[-0.10831565]	0.000	NaN	NaN	NaN	NaN	NaN	1.920	0.0	5.673	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[57.45015391]	0.000	NaN	NaN	NaN	NaN	0.534	0.000	0.0	0.845	0.429	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.25216507]	0.000	NaN	NaN	NaN	NaN	0.000	0.000	0.0	0.384	0.388	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	0.793	0.0	[2.621351]	0.001	NaN	NaN	NaN	NaN	NaN	0.761	0.0	1.043	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.002	0.0	[133.75408424]	0.000	NaN	NaN	NaN	NaN	0.300	0.003	0.0	0.548	0.113	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[25.14184595]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.122	0.094	See	See

Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.176	0.0	0.455	0.0	NaN	NaN	NaN	0.177	0.000	0.991	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.011	0.0	7.152	0.0	NaN	NaN	0.109	0.018	0.001	0.625	0.030	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.0	1.092	0.0	NaN	NaN	NaN	0.000	0.000	0.677	0.611	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.356	0.0	0.590	0.0	NaN	NaN	NaN	0.227	0.000	5.965	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.0	4.444	0.0	NaN	NaN	1.000	0.000	0.000	0.810	0.920	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.0	0.013	0.0	NaN	NaN	NaN	0.000	0.000	0.646	0.673	See	See

Gradient boosting: randomized HPO benchmarks

reporting_hpo = ReportingHpo(config="config.yml")
reporting_hpo.run()

Raw results

Smoothed HPO Curves

Speedup barplots

Speedup curves